Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
695
datasets available to search
ShareScore release 0.7.1
Dataset results
695 results for “topologies”
Random models for CASCADE 2.0 topology and Drabme simulation results
<p>For the investigation/analysis based on this dataset, see: <a href="https://druglogics.github.io/gitsbe-model-analysis/cascade/random-model-ss/main.html">https://druglogics.github.io/gitsbe-model-analysis/cascade/random-model-ss/main.html</a></p>
Single-gene imaging links genome topology, promoter–enhancer communication and transcription control
<p>This repository contains experimental data and code supporting the publication: Li <em>et al.</em> (2020) Single-gene imaging links genome topology, promoter–enhancer communication and transcription control. <em>Nat. Struct. Mol. Biol., </em>DOI:https://doi.org/10.1038/s41594-020-0493-6</p> <p>Details about the files are provided in the Readme files in the associated sub-folders.</p>
Data from: Topological data analysis of biological aggregation models
We apply tools from topological data analysis to two mathematical models inspired by biological aggregations such as bird flocks, fish schools, and insect swarms. Our data consists of numerical simulation output from the models of Vicsek and D'Orsogna. These models are dynamical systems describing the movement of agents who interact via alignment, attraction, and/or repulsion. Each simulation time frame is a point cloud in position-velocity space. We analyze the topological structure of these point clouds, interpreting the persistent homology by calculating the first few Betti numbers. These Betti numbers count connected components, topological circles, and trapped volumes present in the data. To interpret our results, we introduce a visualization that displays Betti numbers over simulation time and topological persistence scale. We compare our topological results to order parameters typically used to quantify the global behavior of aggregations, such as polarization and angular momentum. The topological calculations reveal events and structure not captured by the order parameters.
Data from: Modeling of kidney hemodynamics: probability-based topology of an arterial network
Through regulation of the extracellular fluid volume, the kidneys provide important long-term regulation of blood pressure. At the level of the individual functional unit (the nephron), pressure and flow control involves two different mechanisms that both produce oscillations. The nephrons are arranged in a complex branching structure that delivers blood to each nephron and, at the same time, provides a basis for an interaction between adjacent nephrons. The functional consequences of this interaction are not understood, and at present it is not possible to address this question experimentally. We provide experimental data and a new modeling approach to clarify this problem. To resolve details of microvascular structure, we collected 3D data from more than 150 afferent arterioles in an optically cleared rat kidney. Using these results together with published micro-computed tomography (μCT) data we develop an algorithm for generating the renal arterial network. We then introduce a mathematical model describing blood flow dynamics and nephron to nephron interaction in the network. The model includes an implementation of electrical signal propagation along a vascular wall. Simulation results show that the renal arterial architecture plays an important role in maintaining adequate pressure levels and the self-sustained dynamics of nephrons.
Data from: The topology of a discussion: the #occupy case
Introduction: We analyse a large sample of the Twitter activity that developed around the social movement 'Occupy Wall Street', to study the complex interactions between the human communication activity and the semantic content of a debate. Methods: We use a network approach based on the analysis of the bipartite graph @Users-#Hashtags and of its projections: the 'semantic network', whose nodes are hashtags, and the 'users interest network', whose nodes are users. In the first instance, we find out that discussion topics (#hashtags) present a high structural heterogeneity, with a relevant role played by the semantic hubs that are responsible to guarantee the continuity of the debate. In the users' case, the self-organisation process of users' activity, leads to the emergence of two classes of communicators: the 'professionals' and the 'amateurs'. Results: Both the networks present a strong community structure, based on the differentiation of the semantic topics, and a high level of structural robustness when certain sets of topics are censored and/or accounts are removed. Conclusions: By analysing the characteristics of the dynamical networks we can distinguish three phases of the discussion about the movement. Each phase corresponds to a specific moment of the movement: from declaration of intent, organisation and development and the final phase of political reactions. Each phase is characterised by the presence of prototypical #hashtags in the discussion.
Data Catalog for: Flux-induced topological superconductivity in full-shell nanowires
<p>Data Catalog for: Flux-induced topological superconductivity in full-shell nanowires</p>
Data for "Phonon thermal Hall effect in charge-compensated topological insulators" published in PRB 109, 104304 (2024), Editors' Suggestion
<p>The manuscript of this <a href="https://doi.org/10.1103/PhysRevB.109.104304">Phys. Rev. B paper</a> is also available on arxiv <a href="https://arxiv.org/abs/2401.03064">2401.03064</a>. Note, however that the order of the Figures 6-9 in the Appendix differs between the PRB version and the arxiv version. The naming of the data sets stored here on Zenodo refer to the Figure numbering of the arxiv version.</p>
DomainMapper: Accurate Domain Structure Annotation Including Those with Non-contiguous Topologies
<p>Mapped domains of all proteomes performed in the study "DomainMapper: Accurate Domain Structure Annotation Including Those with Non-contiguous Topologies"</p> <p>All files were created with DomainMapper 3.0.1</p>
Output files for 'Multiscale topology classifies cells in subcellular spatial transcriptomics'
<p>This data set contains output files for the paper 'Multiscale topology classifies cells in subcellular spatial transcriptomics'. See the READMEs included for each experiment subdirectory.</p>
Evolving geometries, topologies, and apertures in fracture networks: Quantitative insights from lattice modeling
<p>Fracture networks are crucial in controlling rock mass permeability. Some of the important features of the fracture networks like the density, interconnectivity, spatial distribution, and fracture apertures determine the success of the subsurface operations. Fracture networks can be studied with analogue studies, physical experiments, and numerical modeling. In this study, we analyse the evolution of a two-dimensional fracture network under gravitational and shear loads using the lattice modeling capabilities of the microstructural modeling environment “Elle”. The simulation cases include varying gravitational loads and Young’s moduli of the formations. The topological progression of the modeled fracture network from isolated to interconnected nodes depicts a realistic network evolution process. The study shows that the rock stiffness exhibits a direct correlation with the number of fractures influencing the average aperture size of the network. A higher gravity load resulted in the development of a sparse fracture network. Stiffer rock models also showed an early onset of fracturing.</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCA P1)
<p>This repository contains the dataset for the Missing PCA P1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Source Data File for "Magnetocaloric Effect of Topological Excitations in Kitaev Magnets" published in Nat. Commun.
<table> <tbody> <tr> <td> <p><span>“Source Data.xlsx” for manuscript "</span>Magnetocaloric Effect of Topological Excitations in Kitaev Magnets"<span> is provided. Here is the title and a brief description for the data:</span></p> <p> </p> <p><span>Sheet 1: Fig1bcd(gh)</span></p> <p><span>Description: The data matrix in three-dimensional space for the landscape of isentropes as shown in Fig1.b-d. The data could also be used in generating Fig1.gh.</span></p> <p><span> </span></p> <p><span>Sheet 2: Fig1e</span></p> <p><span>Description: The data to generate Fig.1e. The rows starting with Gamma_B represent the Gruneisen parameter; while the rows starting with B indicate the magnetic fields. </span></p> <p><span> </span></p> <p><span>Sheet 3: Fig1f</span></p> <p><span>Description: The data to generate Fig.1f. The rows starting with T represent the temperature; while the rows starting with chi indicate the magnetic susceptibility. </span></p> <p><span> </span></p> <p><span>Sheet 4: Fig1gh</span></p> <p><span>Description: The data to generate Fig.1gh. The rows starting with T represent the temperature; while the rows starting with S/ln2 indicate the thermal entropy. Other relevant data could be found in Sheet 1.</span></p> <p><span> </span></p> <p><span>Sheet 5: Fig2ab</span></p> <p><span>Description: The data matrix in three-dimensional space for the landscape of isentropes as shown in Fig.2a. These data also include the portion used to generate Fig.2b.</span></p> <p><span> </span></p> <p><span>Sheet 6: Fig2cd</span></p> <p><span>Description: The data matrix in three-dimensional space for the landscape of isentropes as shown in Fig.2c. These data also include the portion used to generate Fig.2d.</span></p> <p><span> </span></p> <p><span>Sheet 7: Fig3ab</span></p> <p><span>Description: The data to generate Fig.3ab. The rows starting with T represent the temperature; rows starting with Cm represent the specific heat; rows starting with Wp represent the expectation value of flux operator; while the rows starting with S/ln2 indicate the thermal entropy. </span></p> <p><span> </span></p> <p><span>Sheet 8: Fig3c</span></p> <p><span>Description: The data to generate Fig.3c. The rows starting with T represent the temperature; while the rows starting with S1(omega=0) indicate the estimate of relaxation rate. </span></p> <p><span> </span></p> <p><span>Sheet 9: Fig3d</span></p> <p><span>Description: The data to generate the landscape of Fig.3d. The columns starting with kx/pi and ky/pi represent the q-points in momentum space; while the columns starting with S(q) and S_tr(q) indicate the data of spin structure factors. </span></p> <p><span> </span></p> <p><span>Sheet 10: Fig3e</span></p> <p><span>Description: The data matrix in three-dimensional space for the landscape of specific heat as shown in Fig.3e</span></p> <p><span> </span></p> <p><span>Sheet 11: Fig4a</span></p> <p><span>Description: The data to generate Fig.4a. The rows starting with T represent the temperature; while the rows starting with S/ln2 indicate the thermal entropy. Other relevant data could be found in Sheet 1.</span></p> <p><span> </span></p> <p><span>Sheet 12: Fig4c</span></p> <p><span>Description: The data to generate Fig.4c. The rows starting with Ti represent the initial temperature; while the rows starting with Tf indicate the final reached temperature. </span></p> <p><span> </span></p> <p><span>Sheet 13: Fig4d</span></p> <p><span>Description: The data to generate Fig.4d. The rows starting with T tilde represent the rescaled temperature; while the rows starting with S/ln(2s+1) indicate the renormalized entropy. Other relevant data could be found in Sheet 1.</span></p> <p><span> </span></p> <p><span> </span></p> <p><span> </span></p> </td> </tr> </tbody> </table>
Data from: Topological materials discovery by large-order symmetry indicators
Crystalline symmetries play an important role in the classification of band structures, and their richness leads to various topological crystalline phases. On the basis of our recently developed method for the efficient discovery of topological materials using symmetry indicators, we explore topological materials in five space groups, which are diagnosed by large-order symmetry indicators and support the coexistence of several kinds of gapless boundary states in a single compound. We predict many candidate materials; some representatives include Pt3Ge, graphite, XPt3, Au4Ti, and Ti2Sn. As by-products, we also find that AgXF3 and AgAsX are good Dirac semimetals with clean Fermi surfaces. The proposed materials provide a good platform for studying the novel properties emerging from the interplay between different types of boundary states.
Dataset of spatial transcriptomics of lung adenocarcinoma for analyzing the tumor microenvironment using topological analysis
<p>The human lung adenocarcinoma dataset for 'STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data'. </p>
Weighted Link Schedules in 100-node Random Topology Wireless Networks
<p>This is a data repo for the data samples used for learning the link scheduling in a randomly placed networks. This data set contains samples for 100-node networks, and the scheduling decisions are made from delayed column generation (DCG) algorithm.</p> <p>random_weighted_flow_100_5.tar.xz: </p>
Non-local transport signatures of topological superconductivity in a phase-biased planar Josephson junction - code
<p>Here are the codes to generate the whole data of the paper.</p>
Dirac Bands in the Topological Insulator Bi2Se3 Mapped by Time-Resolved Momentum Microscopy
<p>Supporting data</p>
Exploring the Visual Sensitivity for Topological Properties in Newborn Infants
ClinicalTrials.gov study NCT01330537. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessment of Eyelid Topology and Kinetics Based on Deep Learning Method
ClinicalTrials.gov study NCT04921020. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Clinical Survey of Different Abutment Topologies
ClinicalTrials.gov study NCT02304692. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.